CN111008563B - Dim light scene seed germination detection method and device and readable storage medium - Google Patents
Dim light scene seed germination detection method and device and readable storage medium Download PDFInfo
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Abstract
The invention provides a method and a device for detecting seed germination in a dim light scene and a readable storage medium, wherein the method comprises the following steps: acquiring an original germination state image of seeds in a seed incubator; preprocessing the original germination state image to obtain an original germination state gray level image; processing the original germination state gray level image according to a preset method to obtain an effective germination state image; extracting all independent boundary sub-images in the effective germination state image; judging the germination state corresponding to each independent boundary sub-image according to a preset rule; outputting germination information of seeds corresponding to the original germination state images according to the germination state corresponding to each independent boundary sub-image. The problem that the traditional germination rate detection method is high in labor cost and the observation result is easily interfered by subjective factors of inspectors is solved.
Description
Technical Field
The invention relates to the field of agricultural experimental equipment control, in particular to a dim light scene seed germination detection method and device and a readable storage medium.
Background
The germination rate is an important index for checking the quality of seeds, and the safety and benefit of agricultural production can be ensured only by adopting seeds with high germination rate for production. During the cultivation of seeds, the germination rate of different seeds at different times is continuously observed, a germination curve is constructed, and the best seeds are selected.
Conventional breeding processes begin with seeds being placed in an incubator until the seeds have germinated, continuously over a period of 7 x 24 hours, with a few hours apart requiring a worker to remove the petri dishes from the incubator and manually count the number of germinated seeds. Therefore, the traditional germination rate detection method has high labor cost, and the observation result is easily interfered by subjective factors of inspectors.
Disclosure of Invention
Aiming at the problems that the germination rate detection method in the prior art has high labor cost, and the observation result is easily interfered by subjective factors of inspectors, the invention provides a dim light scene seed germination detection method, a dim light scene seed germination detection device and a readable storage medium.
The invention provides a method for detecting germination of seeds in a dim light scene, which comprises the following steps:
acquiring an original germination state image of seeds in a seed incubator;
preprocessing the original germination state image to obtain an original germination state gray level image;
processing the original germination state gray level image according to a preset method to obtain an effective germination state image;
extracting all independent boundary sub-images in the effective germination state image;
judging the germination state corresponding to each independent boundary sub-image according to a preset rule;
outputting germination information of seeds corresponding to the original germination state images according to the germination state corresponding to each independent boundary sub-image.
Further, the original germination state image of the seeds in the seed incubator is acquired in real time through a dim light camera, or is acquired by intercepting the image according to the germination video of the seeds shot by the dim light camera in the seed incubator.
Further, the original sprouting state image is preprocessed to obtain an original sprouting state gray level image, and the method specifically comprises the following steps:
acquiring a camera parameter matrix and distortion parameters of a darklight camera in the seed incubator by adopting a Zhang Zhengyou calibration method;
carrying out distortion correction on the original germination state image according to the camera parameter matrix and the distortion parameters to obtain a corrected original germination state image;
and converting the corrected original germination state image into an original germination state gray scale image.
Further, the original germination state gray level image is processed according to a preset method to obtain an effective germination state image, and the method specifically comprises the following steps:
performing adaptive thresholding on the original sprouting state gray level image by using an adaptive thresholding function provided by OpenCV;
and performing preset times of corrosion operation and preset times of expansion operation on the original sprouting state gray level image subjected to the self-adaptive threshold processing by utilizing a rectangular convolution check of a preset specification to obtain an effective sprouting state image.
Further, judging the germination state corresponding to each independent boundary sub-image according to a preset rule, and specifically comprising the following steps:
fitting each independent boundary sub-image respectively to obtain a minimum circumcircle corresponding to each independent boundary sub-image;
when the diameter of the minimum circumscribed circle is larger than the diameter set by a user, marking a circumscribed circle corresponding area concentric with the minimum circumscribed circle and having a radius which is a preset multiple of the radius of the minimum circumscribed circle as a seed germination verification area;
obtaining the number of germination pixel points corresponding to each seed germination verification area in the effective germination state image;
and correspondingly judging the germination state corresponding to each seed germination verification area by using a preset threshold formula according to the number of the germination pixel points corresponding to each seed germination verification area.
Further, according to the number of the germination pixel points corresponding to each seed germination verification area, the germination state corresponding to each seed germination verification area is correspondingly judged by using a preset threshold formula, and the method specifically comprises the following steps:
calculating germination state judgment variables ThFaya in each seed germination verification area according to a formula of ThFaya=RateFaya R, wherein RateFaya is the proportion of the number of germination pixel points corresponding to each seed germination verification area to the total number of pixel points in the seed germination verification area, and R is the preset average radius of seeds to be detected;
and respectively judging the magnitude relation between the germination state judgment variable ThFaya and a preset judgment value in each seed germination verification area, and marking that the seeds corresponding to the seed germination verification areas are germinated when the value of any one germination state judgment variable ThFaya is larger than the preset judgment value, or marking that the seeds corresponding to the seed germination verification areas are not germinated.
Further, the preset determination value and the average radius of the preset seeds to be detected are input in advance by a user.
Further, outputting germination information of seeds corresponding to the original germination state image according to the germination state corresponding to each independent boundary sub-image, and specifically comprising the following steps:
counting the germination state corresponding to each independent boundary sub-image;
obtaining the germination rate of the seeds to be detected according to the germination state corresponding to each independent boundary sub-image;
outputting the germination rate, and generating a germination rate record file.
The second aspect of the present invention provides a germination detection device for seeds in a dark scene, the germination detection device comprising: a processor and a memory;
the memory is used for storing a computer program;
the processor is configured to implement the dim light scene seed germination detection method as described above when executing the computer program.
A third aspect of the present invention provides a readable storage medium having stored thereon a computer program which, when executed by a processor, implements a dim light scene seed germination detection method as described above.
The dim light scene seed germination detection method and device provided by the invention have the beneficial effects that: setting a darkness camera in a seed incubator, collecting an original germination state image in the seed incubator through the darkness camera, preprocessing and processing the original germination state image to obtain an effective germination state image, extracting all independent boundary sub-images in the effective germination state image, judging the germination state corresponding to each independent boundary sub-image according to a preset rule, and finally outputting germination information of seeds corresponding to the original germination state image according to the germination state corresponding to each independent boundary sub-image. The method has the advantages that the seed incubator does not need to be opened in the process of judging the germination information of the seed incubator, the whole process can be automatically completed, the subjective factor interference of human statistics is eliminated, and the problems that the manpower cost is high and the observation result is easily interfered by the subjective factor of an inspector in the traditional germination rate detection method are solved.
Drawings
Fig. 1 is a schematic flow chart of a method for detecting germination of seeds in a dim light scene according to an embodiment of the invention;
fig. 2 is a schematic diagram of a second flow chart of a method for detecting germination of seeds in a dim light scene according to an embodiment of the invention;
fig. 3 is a schematic diagram of a third flow chart of a method for detecting germination of seeds in a dim light scene according to an embodiment of the invention;
fig. 4 is a fourth flowchart of a method for detecting germination of seeds in a dim light scene according to an embodiment of the present invention;
fig. 5 is a fifth flowchart of a method for detecting germination of seeds in a dim light scene according to an embodiment of the present invention;
fig. 6 is a sixth flowchart of a method for detecting germination of seeds in a dim light scene according to an embodiment of the present invention;
fig. 7 is an original germination state image of a method for detecting germination of seeds in a dim light scene according to an embodiment of the present invention;
FIG. 8 is a graph showing the result of adaptive thresholding performed by the dim light scene seed germination detection method according to an embodiment of the present invention;
FIG. 9 is an image of a dim light scene seed germination detection method according to an embodiment of the present invention after a corrosion and expansion operation;
fig. 10 is a diagram of independent boundary marks of germination verification areas of a method for detecting germination of seeds in a dim light scene according to an embodiment of the present invention;
fig. 11 is a germination status marking chart of a germination detection method for seeds in a dim light scene according to an embodiment of the invention.
Detailed Description
The principles and features of the present invention are described below in connection with examples, which are set forth only to illustrate the present invention and not to limit the scope of the invention.
Aiming at the problems that the labor cost is high and the observation result is easily interfered by subjective factors of inspectors in the traditional germination rate detection method, the invention provides a dim light scene seed germination detection method, a dim light scene seed germination detection device and a readable storage medium.
In one aspect, referring to fig. 1, the method for detecting germination of seeds in a dark scene according to the first embodiment of the present invention includes the following steps:
s1, acquiring an original germination state image of seeds in a seed incubator.
In the step, the original germination state image of the seeds in the seed incubator is acquired in real time through a darkness camera, or is acquired by intercepting the image in the germination video of the seeds shot by the darkness camera in the seed incubator, and the original germination state image acquired by the darkness camera can be sent to a control computer host through a data line. The original germination status image is shown in fig. 7.
S2, preprocessing the original germination state image to obtain an original germination state gray level image.
In this step, the original germination status image is an RGB color image, and because the color image contains a large amount of color information, a large amount of memory space is occupied during image processing, and the running speed of the system is also reduced. Therefore, the color image is required to be converted into the gray image, so that the memory is saved, the running speed of the system is improved, and a foundation is laid for the realization of a subsequent image processing algorithm. The image graying method can be a maximum value method, an average value method and a full average value method; in this embodiment, a weighted average method is used: the three RGB components of the original germination status image are weighted and averaged with different weights according to importance and other indicators. Since the sensitivity of the human eye to green is highest and the sensitivity to blue is lowest, in this embodiment, a more reasonable gray image can be obtained by weighted averaging the three components of RGB according to the following formula: f (i, j) =0.3B (i, j) +0.59G (i, j) +0.11R (i, j). Where B (i, j) is the blue component of the image at the (i, j) coordinate point, G (i, j) is the green component of the image at the (i, j) coordinate point, and R (i, j) is the red component of the image at the (i, j) coordinate point.
And S3, processing the original germination state gray level image according to a preset method to obtain an effective germination state image.
The method for preprocessing after the original germination state image is obtained by the control computer host comprises a mean value filtering method, a Gaussian filtering method and a median filtering method; the average filtering is also called linear filtering, and mainly adopts a neighborhood averaging method to construct a template to process target pixels on an image, wherein the template consists of neighboring pixels around the template. Taking N pixels around the target pixel as the center, thereby forming a template of N pixels, and taking the average value of all pixels in the template to replace the original pixel value. The Gaussian filtering method is a linear smoothing filtering method, and is a process of carrying out weighted average on the whole image, and the value of each pixel point is obtained after the value of each pixel point and other pixels in the neighborhood of the pixel point are subjected to weighted average. The Gaussian filter template has different influences on the point according to the difference of the distance between the pixel and the center, and a weight coefficient is added on the basis of mean value filtering. The median filtering is a nonlinear smoothing filtering that sets the gray value of each pixel point to the median of all pixel gray values within the point neighborhood window. The basic principle is that the value of a point in the digital image is replaced by the median value of the values of points in a neighborhood of the point. And preprocessing the original germination state image by sequentially using an average filtering method, a Gaussian filtering method and a median filtering method. Noise of an acquired image original germination state image caused by poor illumination conditions in a seed incubator due to the fact that a dim light camera is close to a seed shooting distance is reduced.
And S4, extracting all independent boundary sub-images in the effective germination state image.
In this step, the independent boundary sub-images may utilize a differential technique to separate the foreground and the background of the effective sprouting state image, obtain a foreground image, perform convolution operation on the foreground image and the high-pass filtering template to find out a boundary contour, separate all the independent boundary sub-images according to the continuity and the closure of the boundary contour, and perform marking.
And S5, judging the germination state corresponding to each independent boundary sub-image according to a preset rule.
In this step, the preset rule may be whether the ratio of the number of germinated pixels in the corresponding area of the independent boundary sub-image to the total number of pixels in the independent boundary sub-image exceeds a preset value, or whether the number of pixels in the independent boundary sub-image exceeds a preset value, or the like.
And S6, outputting germination information of seeds corresponding to the original germination state images according to the germination state corresponding to each independent boundary sub-image.
In this step, the germination information includes the number of germinated seeds, the number of ungerminated seeds, the germination rate, and the like. The control computer host can control the germination information to be displayed through a screen and also can generate a germination information file, and drive a printer to print the germination information file.
The dim light scene seed germination detection method and device provided by the invention have the beneficial effects that: setting a darkness camera in a seed incubator, collecting an original germination state image in the seed incubator by the darkness camera, preprocessing and processing the original germination state image to obtain an effective germination state image, extracting all independent boundary sub-images in the effective germination state image, judging the germination state corresponding to each independent boundary sub-image according to a preset rule, and finally outputting germination information of seeds corresponding to the original germination state image according to the germination state corresponding to each independent boundary sub-image. The method has the advantages that the seed incubator does not need to be opened in the process of judging the germination information of the seed incubator, the whole process can be automatically completed, the subjective factor interference of human statistics is eliminated, and the problems that the manpower cost is high and the observation result is easily interfered by the subjective factor of an inspector in the traditional germination rate detection method are solved.
Specifically, referring to fig. 2, in a second embodiment of the present invention, the step S2 of preprocessing the original germination status image to obtain an original germination status gray scale image specifically includes:
s21, acquiring a camera parameter matrix and distortion parameters of the darklight camera in the seed incubator by adopting a Zhang Zhengyou calibration method.
In this step, the Zhang Zhengyou calibration method, zhang Zhengyou, 1998 was in the paper: "AFlexible New Technique fro Camera Calibration" proposes a camera calibration method based on a single plane checkerboard.
S22, carrying out distortion correction on the original germination state image according to the camera parameter matrix and the distortion parameters to obtain a corrected original germination state image.
The specific operation of this step may be embodied with reference to patent 201910262696.8.
S23, converting the corrected original germination state image into an original germination state gray scale image.
The step is to convert the corrected RGB image of the original germination state into a gray image to obtain the gray image of the original germination state.
Specifically, referring to fig. 3, in a third embodiment of the present invention, the step S3 of processing the raw germination status gray scale image according to a preset method to obtain an effective germination status image specifically includes the following steps:
and S31, performing adaptive thresholding on the original sprouting state gray level image by using an adaptive thresholding function adaptive provided by OpenCV. The OpenCV is a cross-platform computer vision library based on BSD license (open source) issues, which can run on Linux, windows, android and MacOS operating systems. The system is lightweight and efficient, is composed of a series of C functions and a small number of C++ classes, provides interfaces of Python, ruby, MATLAB and other languages, and realizes a plurality of general algorithms in the aspects of image processing and computer vision. As shown in FIG. 8, the adaptive thresholding is realized by using an adaptive thresholding function adaptive provided by OpenCV because the system needs all-weather use, different time environments have different illumination and sometimes have uneven illumination. The function adopts the local threshold value to divide the image, the dividing effect is not influenced by the illumination change of the whole environment, the influence of illumination unevenness is also smaller, and the reference codes are as follows:
adaptiveThreshold(srcGray,threshold_output,255,CV_ADAPTIVE_THRESH_MEAN_C,CV_THRESH_BINARY,31,10)。
s32, performing preset times of corrosion operation and preset times of expansion operation on the original sprouting state gray level image subjected to self-adaptive threshold processing by utilizing a rectangular convolution check of a preset specification, and obtaining an effective sprouting state image.
From the threshold segmentation result, the seed region is affected by the light-colored buds, and the hollow region appears in the seed region to affect the extraction of seeds, so that the region needs to be connected by morphology to obtain the seed region, and the method comprises the following steps:
the images were checked for erosion and dilation operations using a 15 x 15 rectangular convolution: etching for three times, removing larger particle noise, expanding for three times, recovering lost seed area, and connecting the broken seed area caused by germination. The image after the etching and swelling operation is shown in figure 9,
specifically, referring to fig. 4, in a fourth embodiment of the present invention, the determining the germination status S5 corresponding to each of the independent boundary sub-images according to the preset rule specifically includes the following steps:
s51, fitting each independent boundary sub-image respectively to obtain a minimum circumcircle corresponding to each independent boundary sub-image;
after the binary image is acquired, all contours are extracted and surrounded by a minimum circle for each contour. When the fitted circle radius is within a certain range, the fitted circle radius is considered as a seed area, and the upper and lower limit thresholds related to the range are preset according to specific scenes.
And S52, when the diameter of the minimum circumscribed circle is larger than the diameter set by a user, marking a circumscribed circle corresponding area which is concentric with the minimum circumscribed circle and has a radius which is a preset multiple of the radius of the minimum circumscribed circle as a seed germination verification area.
After the seed region is obtained, the radius of the circle is enlarged 1.5 times to form a new circle region, and the length of the seed is analyzed in the region to determine whether the seed germinates or not in order to ensure that the circle contains the whole region of the seed and the bud. Fig. 10 is a diagram of independent boundary marks of germination verification areas of a method for detecting germination of seeds in a dim light scene according to an embodiment of the invention;
s53, obtaining the number of germination pixel points corresponding to each seed germination verification area in the effective germination state image;
firstly, the software automatically counts the gray average value of the background, which is assumed to be ThBack, and in the system, the seeds are required to be dark, the buds are required to be light, and the background is between the two.
And then, carrying out image binarization in each new circle region, when the gray level of a certain point is greater than the RateBack multiple of ThBack, taking the buds as the buds, filling the buds as white, otherwise, taking the buds as black, wherein the parameter ThBack is obtained by software automatic analysis, rateBack is a preset value, generally, the parameter selection is between 1.5 and 1.9, and the number of the buds obtained is more or less.
S54, according to the number of the germination pixel points corresponding to each seed germination verification area, correspondingly judging the germination state corresponding to each seed germination verification area by using a preset threshold formula.
Specifically, referring to fig. 5, in a fifth embodiment of the present invention, the determining, according to the number of the germination pixels corresponding to each of the seed germination verification areas, the germination status S54 corresponding to each of the seed germination verification areas by using a preset threshold formula specifically includes the following steps:
s541, calculating germination state judgment variables ThFaya in each seed germination verification area according to a formula of ThFaya=RateFaya R, wherein RateFaya is the proportion of the number of germination pixels corresponding to each seed germination verification area to the total number of pixels in the seed germination verification area, and R is the average radius of the preset seeds to be detected.
S542, respectively judging the relation between the germination state judgment variable ThFaya and a preset judgment value in each seed germination verification area, and when the value of the germination state judgment variable ThFaya is larger than the judgment value, marking that the seeds corresponding to the seed germination verification area are germinated, otherwise, marking that the seeds corresponding to the seed germination verification area are not germinated. Fig. 11 is a germination status marking chart of a germination detection method for seeds in a dark scene according to an embodiment of the invention, and the thicker circles mark the germinated seeds.
Specifically, referring to fig. 6, in a sixth embodiment of the present invention, the outputting the germination information S6 of the seeds corresponding to the original germination status image according to the germination status corresponding to each of the independent boundary sub-images includes the following steps:
s61, counting the germination state corresponding to each independent boundary sub-image.
The number of independent boundary sub-images corresponding to germination and the number of independent boundary sub-images not corresponding to germination are counted.
And S62, obtaining the germination rate of the seeds to be detected according to the germination state corresponding to each independent boundary sub-image.
In this step, the germination rate, i.e., the number of germinated seeds divided by the total number of seeds.
And S63, outputting the germination rate, and generating a germination rate record file.
In the step, the control computer host can control the germination rate to generate a germination rate record file through screen display, and drive a printer to print the germination rate record file.
In a second aspect, to achieve the above object, an embodiment of the present invention further provides a device for detecting germination of seeds in a dark scene, where the device includes: a processor and a memory;
the memory is used for storing a computer program;
the processor is configured to implement the dim light scene seed germination detection method as described above when executing the computer program.
In a third aspect, to achieve the above object, an embodiment of the present invention further proposes a readable storage medium having stored thereon a computer program which, when executed by a processor, implements the dim light scene seed germination detection method as described above.
The reader should understand that in the description of this specification, a description of the terms "aspect," "alternative embodiments," or "some embodiments," etc., means that a particular feature, step, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention, and the terms "first" and "second," etc., are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any particular order of such features. Thus, a feature defining "first" and "second" etc. may explicitly or implicitly include at least one such feature.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (6)
1. The method for detecting germination of the seeds in the dim light scene is characterized by comprising the following steps of:
acquiring an original germination state image of seeds in a seed incubator;
preprocessing the original germination state image to obtain an original germination state gray level image;
processing the original germination state gray level image according to a preset method to obtain an effective germination state image;
extracting all independent boundary sub-images in the effective germination state image;
judging the germination state corresponding to each independent boundary sub-image according to a preset rule;
outputting germination information of seeds corresponding to the original germination state images according to the germination state corresponding to each independent boundary sub-image;
preprocessing the original germination state image to obtain an original germination state gray level image, and specifically comprising the following steps:
acquiring a camera parameter matrix and distortion parameters of a darklight camera in the seed incubator by adopting a Zhang Zhengyou calibration method;
carrying out distortion correction on the original germination state image according to the camera parameter matrix and the distortion parameters to obtain a corrected original germination state image;
converting the corrected original germination state image into an original germination state gray scale image;
the original germination state gray level image is processed according to a preset method to obtain an effective germination state image, and the method specifically comprises the following steps:
performing adaptive thresholding on the original sprouting state gray level image by using an adaptive thresholding function provided by OpenCV;
performing preset times of corrosion operation and preset times of expansion operation on the original sprouting state gray level image subjected to self-adaptive threshold processing by utilizing a rectangular convolution check with preset specification to obtain an effective sprouting state image;
judging the germination state corresponding to each independent boundary sub-image according to a preset rule, and specifically comprising the following steps:
fitting each independent boundary sub-image respectively to obtain a minimum circumcircle corresponding to each independent boundary sub-image;
when the diameter of the minimum circumscribed circle is larger than the diameter set by a user, marking a circumscribed circle corresponding area concentric with the minimum circumscribed circle and having a radius which is a preset multiple of the radius of the minimum circumscribed circle as a seed germination verification area;
obtaining the number of germination pixel points corresponding to each seed germination verification area in the effective germination state image;
according to the number of the germination pixel points corresponding to each seed germination verification area, correspondingly judging the germination state corresponding to each seed germination verification area by using a preset threshold formula;
according to the number of the germination pixel points corresponding to each seed germination verification area, the germination state corresponding to each seed germination verification area is correspondingly judged by using a preset threshold formula, and the method specifically comprises the following steps:
calculating germination state judgment variables ThFaya in each seed germination verification area according to a formula of ThFaya=RateFaya R, wherein RateFaya is the proportion of the number of germination pixel points corresponding to each seed germination verification area to the total number of pixel points in the seed germination verification area, and R is the preset average radius of seeds to be detected;
and respectively judging the magnitude relation between the germination state judgment variable ThFaya and a preset judgment value in each seed germination verification area, and marking that the seeds corresponding to the seed germination verification areas are germinated when the value of any one germination state judgment variable ThFaya is larger than the preset judgment value, or marking that the seeds corresponding to the seed germination verification areas are not germinated.
2. The method for detecting germination of seeds in a darkness scene according to claim 1, wherein the original germination status image of the seeds in the seed incubator is obtained by capturing the image in real time by a darkness camera or by capturing the image from the germination video of the seeds captured by the darkness camera in the seed incubator.
3. The method for detecting germination of seeds in a dim light scene according to claim 1, wherein the preset determination value and the average radius of the seeds to be detected are input in advance by a user.
4. The method for detecting germination of seeds in a dim light scene according to claim 1, wherein the germination information of seeds corresponding to the original germination status image is outputted according to the germination status corresponding to each of the independent boundary sub-images, comprising the steps of:
counting the germination state corresponding to each independent boundary sub-image;
obtaining the germination rate of the seeds to be detected according to the germination state corresponding to each independent boundary sub-image;
outputting the germination rate, and generating a germination rate record file.
5. A detection device, characterized in that the detection device comprises a processor and a memory;
the memory is used for storing a computer program;
the processor for implementing the dim light scene seed germination detection method according to any one of claims 1 to 4, when executing the computer program.
6. A readable storage medium having stored thereon a computer program which, when executed by a processor, implements the dim light scene seed germination detection method according to any one of claims 1 to 4.
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| CN113207359A (en) * | 2021-06-18 | 2021-08-06 | 深圳市万卉园景观工程有限公司 | Method for improving germination rate of sugarcoated meadow for recovering vegetation on bare slope |
| CN113826542A (en) * | 2021-08-16 | 2021-12-24 | 湖南米米梦工场科技股份有限公司 | Production method of germinated brown rice |
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